Hybrid Fault Diagnosis Method based on Wavelet Packet Energy Spectrum and SSA-SVM

Abstract

As one of the important components of mechanical equipment, rolling bearing has been widely used, and its motion state affects the safety and performance of equipment. To enhance the fault feature information in the bearing signal and improve the classification accuracy of support vector machine, a hybrid fault diagnosis method based on wavelet packet energy spectrum and SSA-SVM is proposed. Firstly, the wavelet packet decomposition is used to decompose vibration signals to generate frequency band energy spectrum, and the bearing characteristic information is constructed from the energy spectrum to extract and enhance the bearing fault characteristic information. Secondly, the penalty and kernel parameters are optimized globally by sparrow search algorithm to improve the classification accuracy of support vector machine, and then construct the WPES-SSA-SVM model. Finally, the proposed model is used to diagnose and analyze the measured signals. Compared with BP, ELM and SVM, the effectiveness and superiority of the proposed method are verified.